In [1]:
import statsmodels
from statsmodels.graphics.tsaplots import plot_pacf
from statsmodels.graphics.tsaplots import plot_acf
from datetime import datetime
import matplotlib as mpl
from dateutil.parser import parse
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objs as pltly
from plotly.offline import iplot
import plotly.graph_objects as go
import plotly.figure_factory as ff
from plotly.subplots import make_subplots
import plotly
import pathlib
import itertools
pd.options.display.max_rows = None
pd.set_option('display.max_columns', 500)
In [2]:
dfpd = pd.read_csv("data/paperMachine/processminer-rare-event-mts - tag-map.csv")
dfp = pd.read_csv("data/paperMachine/processminer-rare-event-mts - data.csv")
dfp.rename(columns={'y': 'label'}, inplace=True)
In [3]:
#test git after test ignore
In [4]:
dfp.head()
Out[4]:
time label x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x24 x25 x26 x27 x28 x29 x30 x31 x32 x33 x34 x35 x36 x37 x38 x39 x40 x41 x42 x43 x44 x45 x46 x47 x48 x49 x50 x51 x52 x53 x54 x55 x56 x57 x58 x59 x60 x61
0 5/1/99 0:00 0 0.376665 -4.596435 -4.095756 13.497687 -0.118830 -20.669883 0.000732 -0.061114 -0.059966 -0.038189 0.877951 -0.052959 -13.306135 0.101068 0.041800 0.199901 -2.327329 -0.944167 3.075199 0.123154 -0.104334 -0.570710 -9.784456 0.355960 15.842819 -0.451973 -0.105282 96 -134.27786 0.058726 -0.021645 9.366755 0.002151 -69.187583 4.232571 -0.225267 -0.196872 -0.072449 -0.103732 -0.720746 -5.412436 76.679042 -0.632727 1351.63286 -0.657095 -0.434947 -108.77597 0.084856 10.210182 11.295155 29.984624 10.091721 0.053279 -4.936434 -24.590146 18.515436 3.473400 0.033444 0.953219 0.006076 0
1 5/1/99 0:02 0 0.475720 -4.542502 -4.018359 16.230659 -0.128733 -18.758079 0.000732 -0.061114 -0.059966 -0.038189 0.873273 -0.014244 -13.306135 0.101108 0.041447 0.304313 -2.340627 -0.939994 3.075199 0.123154 -0.104334 -0.574861 -9.784456 0.360160 16.491684 -0.450451 -0.092430 96 -134.48019 0.058759 -0.004579 9.350215 0.002149 -68.585197 4.311490 -0.225267 -0.196872 -0.059103 -0.083895 -0.720746 -8.343222 78.181598 -0.632727 1370.37895 -0.875629 -1.125819 -108.84897 0.085146 12.534340 11.290761 29.984624 10.095871 0.062801 -4.937179 -32.413266 22.760065 2.682933 0.033536 1.090502 0.006083 0
2 5/1/99 0:04 0 0.363848 -4.681394 -4.353147 14.127998 -0.138636 -17.836632 0.010803 -0.061114 -0.030057 -0.018352 1.004910 0.065150 -9.619596 0.101148 0.041095 0.252839 -2.353925 -0.935824 3.075199 0.123154 -0.104334 -0.579013 -9.784456 0.364356 15.972885 -0.448927 -0.097144 96 -133.94659 0.058791 -0.084658 9.037409 0.002148 -67.838187 4.809914 -0.225267 -0.186801 -0.048696 -0.073823 -0.720746 -1.085166 79.684154 -0.632727 1368.12309 -0.037775 -0.519541 -109.08658 0.085436 18.582893 11.286366 29.984624 10.100265 0.072322 -4.937924 -34.183774 27.004663 3.537487 0.033629 1.840540 0.006090 0
3 5/1/99 0:06 0 0.301590 -4.758934 -4.023612 13.161567 -0.148142 -18.517601 0.002075 -0.061114 -0.019986 -0.008280 0.930037 -0.067199 -15.196531 0.101188 0.040742 0.072873 -2.367223 -0.931651 3.075199 0.123154 -0.104334 -0.583165 -9.784456 0.368556 15.608688 -0.447404 -0.160073 96 -134.00259 0.058824 -0.055118 9.020625 0.002146 -67.091148 5.308343 -0.225267 -0.186801 -0.047017 -0.063752 -0.720746 6.172891 81.186702 -0.632727 1365.69145 -0.987410 0.674524 -109.56277 0.085726 17.719032 11.281972 29.984624 10.104660 0.081600 -4.938669 -35.954281 21.672449 3.986095 0.033721 2.554880 0.006097 0
4 5/1/99 0:08 0 0.265578 -4.749928 -4.333150 15.267340 -0.155314 -17.505913 0.000732 -0.061114 -0.030057 -0.008280 0.828410 -0.018472 -14.609266 0.101229 0.040390 0.171033 -2.380521 -0.927478 3.075199 0.123154 -0.104334 -0.587316 -9.784456 0.372756 15.606125 -0.445879 -0.131630 96 -133.14571 0.058856 -0.153851 9.344233 0.002145 -65.991813 5.806771 -0.225267 -0.186801 -0.057088 -0.063752 -0.720746 -3.379599 82.689258 -0.632727 1363.25786 -0.238445 -0.063044 -110.03891 0.086016 16.855202 11.277577 29.984624 10.109054 0.091121 -4.939414 -37.724789 21.907251 3.601573 0.033777 1.410494 0.006105 0
In [5]:
dfp.columns
Out[5]:
Index(['time', 'label', 'x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x9',
       'x10', 'x11', 'x12', 'x13', 'x14', 'x15', 'x16', 'x17', 'x18', 'x19',
       'x20', 'x21', 'x22', 'x23', 'x24', 'x25', 'x26', 'x27', 'x28', 'x29',
       'x30', 'x31', 'x32', 'x33', 'x34', 'x35', 'x36', 'x37', 'x38', 'x39',
       'x40', 'x41', 'x42', 'x43', 'x44', 'x45', 'x46', 'x47', 'x48', 'x49',
       'x50', 'x51', 'x52', 'x53', 'x54', 'x55', 'x56', 'x57', 'x58', 'x59',
       'x60', 'x61'],
      dtype='object')
In [6]:
dfp["time"]= pd.to_datetime(dfp["time"])
dfp['time']=dfp['time'].dt.strftime('%m-%d %H:%M')
In [7]:
# dfao["Timestamp"].value_counts()
# values = dfao["Timestamp"].value_counts().keys().tolist()
# counts = dfao["Timestamp"].value_counts().tolist()
# p=pd.DataFrame([counts,values])
# p.T
# p.T.describe()
# p.T.iloc[:,0].sum()
#**
#dfasNormUniq.groupby(['Labels']).corr()
# l1=[0,1,2,3]*2
# x1=[0,1,2,3,4,5,6]
# li=np.repeat(x1,3)
#**
# from cydets.algorithm import detect_cycles
# series = pd.Series(dfas0['Timestamp'].tolist())
# detect_cycles(series)
In [8]:
dfp.head()
Out[8]:
time label x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x24 x25 x26 x27 x28 x29 x30 x31 x32 x33 x34 x35 x36 x37 x38 x39 x40 x41 x42 x43 x44 x45 x46 x47 x48 x49 x50 x51 x52 x53 x54 x55 x56 x57 x58 x59 x60 x61
0 05-01 00:00 0 0.376665 -4.596435 -4.095756 13.497687 -0.118830 -20.669883 0.000732 -0.061114 -0.059966 -0.038189 0.877951 -0.052959 -13.306135 0.101068 0.041800 0.199901 -2.327329 -0.944167 3.075199 0.123154 -0.104334 -0.570710 -9.784456 0.355960 15.842819 -0.451973 -0.105282 96 -134.27786 0.058726 -0.021645 9.366755 0.002151 -69.187583 4.232571 -0.225267 -0.196872 -0.072449 -0.103732 -0.720746 -5.412436 76.679042 -0.632727 1351.63286 -0.657095 -0.434947 -108.77597 0.084856 10.210182 11.295155 29.984624 10.091721 0.053279 -4.936434 -24.590146 18.515436 3.473400 0.033444 0.953219 0.006076 0
1 05-01 00:02 0 0.475720 -4.542502 -4.018359 16.230659 -0.128733 -18.758079 0.000732 -0.061114 -0.059966 -0.038189 0.873273 -0.014244 -13.306135 0.101108 0.041447 0.304313 -2.340627 -0.939994 3.075199 0.123154 -0.104334 -0.574861 -9.784456 0.360160 16.491684 -0.450451 -0.092430 96 -134.48019 0.058759 -0.004579 9.350215 0.002149 -68.585197 4.311490 -0.225267 -0.196872 -0.059103 -0.083895 -0.720746 -8.343222 78.181598 -0.632727 1370.37895 -0.875629 -1.125819 -108.84897 0.085146 12.534340 11.290761 29.984624 10.095871 0.062801 -4.937179 -32.413266 22.760065 2.682933 0.033536 1.090502 0.006083 0
2 05-01 00:04 0 0.363848 -4.681394 -4.353147 14.127998 -0.138636 -17.836632 0.010803 -0.061114 -0.030057 -0.018352 1.004910 0.065150 -9.619596 0.101148 0.041095 0.252839 -2.353925 -0.935824 3.075199 0.123154 -0.104334 -0.579013 -9.784456 0.364356 15.972885 -0.448927 -0.097144 96 -133.94659 0.058791 -0.084658 9.037409 0.002148 -67.838187 4.809914 -0.225267 -0.186801 -0.048696 -0.073823 -0.720746 -1.085166 79.684154 -0.632727 1368.12309 -0.037775 -0.519541 -109.08658 0.085436 18.582893 11.286366 29.984624 10.100265 0.072322 -4.937924 -34.183774 27.004663 3.537487 0.033629 1.840540 0.006090 0
3 05-01 00:06 0 0.301590 -4.758934 -4.023612 13.161567 -0.148142 -18.517601 0.002075 -0.061114 -0.019986 -0.008280 0.930037 -0.067199 -15.196531 0.101188 0.040742 0.072873 -2.367223 -0.931651 3.075199 0.123154 -0.104334 -0.583165 -9.784456 0.368556 15.608688 -0.447404 -0.160073 96 -134.00259 0.058824 -0.055118 9.020625 0.002146 -67.091148 5.308343 -0.225267 -0.186801 -0.047017 -0.063752 -0.720746 6.172891 81.186702 -0.632727 1365.69145 -0.987410 0.674524 -109.56277 0.085726 17.719032 11.281972 29.984624 10.104660 0.081600 -4.938669 -35.954281 21.672449 3.986095 0.033721 2.554880 0.006097 0
4 05-01 00:08 0 0.265578 -4.749928 -4.333150 15.267340 -0.155314 -17.505913 0.000732 -0.061114 -0.030057 -0.008280 0.828410 -0.018472 -14.609266 0.101229 0.040390 0.171033 -2.380521 -0.927478 3.075199 0.123154 -0.104334 -0.587316 -9.784456 0.372756 15.606125 -0.445879 -0.131630 96 -133.14571 0.058856 -0.153851 9.344233 0.002145 -65.991813 5.806771 -0.225267 -0.186801 -0.057088 -0.063752 -0.720746 -3.379599 82.689258 -0.632727 1363.25786 -0.238445 -0.063044 -110.03891 0.086016 16.855202 11.277577 29.984624 10.109054 0.091121 -4.939414 -37.724789 21.907251 3.601573 0.033777 1.410494 0.006105 0
In [9]:
dfp=dfp.reset_index()
dfp.index=dfp["time"]
dfp.drop(['time','index'], axis=1, inplace=True)
dfpNorm=dfp[dfp["label"]==0]
dfpAnorm=dfp[dfp["label"]!=0]
In [10]:
dfp[:5]
Out[10]:
label x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x24 x25 x26 x27 x28 x29 x30 x31 x32 x33 x34 x35 x36 x37 x38 x39 x40 x41 x42 x43 x44 x45 x46 x47 x48 x49 x50 x51 x52 x53 x54 x55 x56 x57 x58 x59 x60 x61
time
05-01 00:00 0 0.376665 -4.596435 -4.095756 13.497687 -0.118830 -20.669883 0.000732 -0.061114 -0.059966 -0.038189 0.877951 -0.052959 -13.306135 0.101068 0.041800 0.199901 -2.327329 -0.944167 3.075199 0.123154 -0.104334 -0.570710 -9.784456 0.355960 15.842819 -0.451973 -0.105282 96 -134.27786 0.058726 -0.021645 9.366755 0.002151 -69.187583 4.232571 -0.225267 -0.196872 -0.072449 -0.103732 -0.720746 -5.412436 76.679042 -0.632727 1351.63286 -0.657095 -0.434947 -108.77597 0.084856 10.210182 11.295155 29.984624 10.091721 0.053279 -4.936434 -24.590146 18.515436 3.473400 0.033444 0.953219 0.006076 0
05-01 00:02 0 0.475720 -4.542502 -4.018359 16.230659 -0.128733 -18.758079 0.000732 -0.061114 -0.059966 -0.038189 0.873273 -0.014244 -13.306135 0.101108 0.041447 0.304313 -2.340627 -0.939994 3.075199 0.123154 -0.104334 -0.574861 -9.784456 0.360160 16.491684 -0.450451 -0.092430 96 -134.48019 0.058759 -0.004579 9.350215 0.002149 -68.585197 4.311490 -0.225267 -0.196872 -0.059103 -0.083895 -0.720746 -8.343222 78.181598 -0.632727 1370.37895 -0.875629 -1.125819 -108.84897 0.085146 12.534340 11.290761 29.984624 10.095871 0.062801 -4.937179 -32.413266 22.760065 2.682933 0.033536 1.090502 0.006083 0
05-01 00:04 0 0.363848 -4.681394 -4.353147 14.127998 -0.138636 -17.836632 0.010803 -0.061114 -0.030057 -0.018352 1.004910 0.065150 -9.619596 0.101148 0.041095 0.252839 -2.353925 -0.935824 3.075199 0.123154 -0.104334 -0.579013 -9.784456 0.364356 15.972885 -0.448927 -0.097144 96 -133.94659 0.058791 -0.084658 9.037409 0.002148 -67.838187 4.809914 -0.225267 -0.186801 -0.048696 -0.073823 -0.720746 -1.085166 79.684154 -0.632727 1368.12309 -0.037775 -0.519541 -109.08658 0.085436 18.582893 11.286366 29.984624 10.100265 0.072322 -4.937924 -34.183774 27.004663 3.537487 0.033629 1.840540 0.006090 0
05-01 00:06 0 0.301590 -4.758934 -4.023612 13.161567 -0.148142 -18.517601 0.002075 -0.061114 -0.019986 -0.008280 0.930037 -0.067199 -15.196531 0.101188 0.040742 0.072873 -2.367223 -0.931651 3.075199 0.123154 -0.104334 -0.583165 -9.784456 0.368556 15.608688 -0.447404 -0.160073 96 -134.00259 0.058824 -0.055118 9.020625 0.002146 -67.091148 5.308343 -0.225267 -0.186801 -0.047017 -0.063752 -0.720746 6.172891 81.186702 -0.632727 1365.69145 -0.987410 0.674524 -109.56277 0.085726 17.719032 11.281972 29.984624 10.104660 0.081600 -4.938669 -35.954281 21.672449 3.986095 0.033721 2.554880 0.006097 0
05-01 00:08 0 0.265578 -4.749928 -4.333150 15.267340 -0.155314 -17.505913 0.000732 -0.061114 -0.030057 -0.008280 0.828410 -0.018472 -14.609266 0.101229 0.040390 0.171033 -2.380521 -0.927478 3.075199 0.123154 -0.104334 -0.587316 -9.784456 0.372756 15.606125 -0.445879 -0.131630 96 -133.14571 0.058856 -0.153851 9.344233 0.002145 -65.991813 5.806771 -0.225267 -0.186801 -0.057088 -0.063752 -0.720746 -3.379599 82.689258 -0.632727 1363.25786 -0.238445 -0.063044 -110.03891 0.086016 16.855202 11.277577 29.984624 10.109054 0.091121 -4.939414 -37.724789 21.907251 3.601573 0.033777 1.410494 0.006105 0
In [11]:
print(dfp.shape)
print(dfpNorm.shape)
print(dfpAnorm.shape)

(18398, 62)
(18274, 62)
(124, 62)
In [12]:
print("dfp:")
pd.DataFrame(dfp.describe().transpose()).iloc[:,[1,2,3,7]]

dfp:
Out[12]:
mean std min max
label 0.006740 0.081822 0.000000 1.000000
x1 0.011824 0.742875 -3.787279 3.054156
x2 0.157986 4.939762 -17.316550 16.742105
x3 0.569300 5.937178 -18.198509 15.900116
x4 -9.958345 131.033712 -322.781610 334.694098
x5 0.006518 0.634054 -1.623988 4.239385
x6 2.387533 37.104012 -279.408440 96.060768
x7 0.001647 0.108870 -0.429273 1.705590
x8 -0.004125 0.075460 -0.451141 0.788826
x9 -0.003056 0.156047 -0.120087 4.060033
x10 -0.002511 0.106526 -0.098310 2.921802
x11 -0.011166 3.220442 -26.191152 5.774670
x12 0.014964 2.381027 -22.223434 3.813738
x13 1.418065 45.801703 -164.897670 143.876437
x14 0.003222 0.162565 -0.028775 1.999404
x15 0.001107 0.039619 -0.623730 1.990212
x16 -0.075376 2.279129 -19.901141 1.791415
x17 0.138210 2.149115 -14.081542 11.817992
x18 0.105010 1.947106 -18.313006 11.738202
x19 0.463652 4.895061 -156.929680 25.068058
x20 -0.001578 0.302746 -1.520451 6.512321
x21 0.076673 1.317702 -7.106501 6.678016
x22 -0.036124 0.833010 -3.228480 1.771673
x23 -0.088743 5.961967 -236.783570 27.417853
x24 -0.393265 4.734010 -12.411317 109.584594
x25 0.635612 59.847503 -579.412730 19.767685
x26 0.048185 0.995501 -2.333210 3.917840
x27 -0.006214 0.450702 -1.807603 1.165952
x28 100.141646 17.642120 51.000000 139.000000
x29 7.054580 130.112284 -228.302190 312.635010
x30 0.003785 0.088395 -0.262892 0.633217
x31 -0.000296 0.118237 -0.539117 0.492164
x32 -0.400527 62.478214 -608.372930 12.157893
x33 0.000090 0.001070 -0.001809 0.002293
x34 4.525465 75.696562 -508.638880 143.236489
x35 0.050165 1.893939 -4.764285 6.829778
x36 -0.001182 0.268737 -0.945199 6.258630
x37 -0.021693 0.279232 -1.706928 0.892944
x38 -0.004367 0.202733 -0.147118 5.344958
x39 -0.003903 0.178014 -0.163853 2.756158
x40 -0.036444 0.967533 -5.700757 1.069164
x41 -0.006410 4.842681 -58.183432 53.087075
x42 0.266766 57.751465 -39.592770 238.135654
x43 0.070846 4.961169 -0.632727 42.484479
x44 403.959326 2155.005113 -3768.476500 4051.738330
x45 0.090221 4.023940 -2.753967 38.592859
x46 0.032000 0.756007 -2.453742 9.591302
x47 -0.983876 68.300305 -174.148590 141.322785
x48 0.002474 1.099839 -5.484846 4.003038
x49 5.051040 107.164471 -450.744260 533.410530
x50 0.602553 6.454156 -23.448985 17.828847
x51 -3.357339 348.256716 -3652.989000 40.152348
x52 0.380519 6.211598 -187.943440 14.180588
x53 0.360246 14.174273 -1817.595500 11.148006
x54 0.173708 3.029516 -8.210370 6.637265
x55 2.379154 67.940694 -230.574030 287.252017
x56 9.234953 81.274103 -269.039500 252.147455
x57 0.233493 2.326838 -12.640370 6.922008
x58 -0.001861 0.048732 -0.149790 0.067249
x59 -0.061522 10.394085 -100.810500 6.985460
x60 0.001258 0.004721 -0.012229 0.020510
x61 0.001033 0.032120 0.000000 1.000000
In [ ]:
In [13]:
dfpd
Out[13]:
tagid tag-description id
0 DateTime DateTime time
1 FISHER.RL EventReel y
2 P4:ASSA.RS RSashScanAvg x1
3 P4:BLD.C1 CT#1 BLADE PSI x2
4 P4:BLD.C2 P4 CT#2 BLADE PSI x3
5 P4:BLGWFL.MV Bleached GWD Flow x4
6 BRSTRL.MV ShwerTemp x5
7 BSFTPD.MV BlndStckFloTPD x6
8 BW2SCD.C1 C1 BW SPREAD CD x7
9 BW2SCD.RS RS BW SPREAD CD x8
10 BW2SMD.C1 C1 BW SPREAD MD x9
11 BW2SMD.RS RS BW SPREAD MD x10
12 BWSA.C1 C1 BW SCAN AVG x11
13 P4:BWSA.RS RS BW SCAN AVG x12
14 P4:CBFLOW.MV CoatBrkFlo x13
15 P4:CLAY.MV Clay Flow x14
16 COUCHV.MV CouchLoVac x15
17 P4:COUVAC COUCH VAC x16
18 D40161.MV 4PrsTopLd x17
19 D40162.MV 4PrsBotLod x18
20 DRW.CA CalndrDrw x19
21 DRW.D2 2DryrDrw x20
22 DRW.D3 3DryrDrw x21
23 DRW.D4 4DryrDraw x22
24 DRW1.PT 1PrsTopDrw x23
25 DRW4.PB 4PrsBotDrw x24
26 FANPMP FanPmpSpd x25
27 FBHDR.MV FlBxHdrVac x26
28 FLTBOX.MV FlatBxVac x27
29 GRDNUM.TX Grade&Bwt x28
30 GWDFLO.MV UnblGWDFlo x29
31 HBXPH.MV Hdbox pH x30
32 HDBXLV.MV HdBxLiqLvl x31
33 HDTO TotHead" x32
34 HORIZS.MV HorzSlcPos x33
35 KRFLOW.MV KraftFlow x34
36 LOAD.CO CouchLoad x35
37 MO2SCD.C1 C1MoSprdCD x36
38 MO2SCD.RS RSMoSprdCD x37
39 MO2SMD.C1 C1MoSprdMD x38
40 MO2SMD.RS RSMoSprdMD x39
41 MOAV.RL RL MoisAct x40
42 PR1REJ.MV PrScrRjFlo x41
43 RBFLOW.MV RwBrkFlo x42
44 RECFLO.MV RcycFbrFlo x43
45 RETAID.MV RetnAidFlo x44
46 RSHDRG RUSH DRAG x45
47 RUSHDG.MV Rush/Drag x46
48 SILICA.MV SilicaFlo x47
49 SLCTMP.MV HBxSlcTemp x48
50 SODALM.MV SodAlumFlo x49
51 SPD.CO CouchSpd x50
52 SPD.MA MachSpd x51
53 SPD1.PT 1PrsTopSpd x52
54 SPD4.PB 4PrsBotSpd x53
55 STARCH.MV WtNStarFlo x54
56 STKFLO.MV BasWgtFlo x55
57 TMPFLO.MV TMP Flow x56
58 TOTHD.MV HBxTotHead x57
59 TRYCON.MV TrayCons x58
60 UHTMP.RL UpprHdTmpRL x59
61 VERTSL.MV VertSlcPos x60
62 EVT.WP EventPress x61
In [14]:
import xgboost as xg
In [15]:
import imblearn as im
In [ ]:
In [ ]:
In [ ]:
In [16]:
# Draw Plot
def plot_df(df,x,y, title="", xlabel='Value',ylabel='Value', dpi=100):
    plt.figure(figsize=(16,10), dpi=dpi)
    plt.plot(df,x,y, color='tab:red')
    plt.gca().set(title=title, xlabel=xlabel, ylabel=ylabel)
    plt.show()
In [17]:
dfp.plot(subplots=True, figsize=(20,40),sharex =False)
Out[17]:
array([<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68B346988>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BBE51C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BC19C48>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BC52D88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BC8AE88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BCC0F88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BD03FC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BD38188>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BD431C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BD7C388>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BDE0508>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BE18608>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BE50688>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BE877C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BEBE8C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BEF79C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BF2FB08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BF69B88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BF9FC88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BFD8DC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C00FEC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C049FC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C0860C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C0BD1C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C0F52C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C12E408>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C166508>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C19E5C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C1D7708>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C20E848>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C245948>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C27FA48>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C2B5B88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C2EBBC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C321CC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C35DE08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C394F08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C3D1048>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C40A108>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C443208>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C479308>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C4B1448>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C4E9548>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C521608>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C55A748>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C592848>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C5CA948>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C603A88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C639B88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C671C08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C6A9D08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C6E1E48>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C719F48>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C758088>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C791188>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C7C8288>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C800388>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C8384C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C86F5C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C8A8688>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C8E07C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68C9188C8>],
      dtype=object)
In [18]:
dfpNorm.plot(subplots=True, figsize=(20,40),sharex=True)
Out[18]:
array([<matplotlib.axes._subplots.AxesSubplot object at 0x000002C68B504B08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68B8B9088>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68B562748>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68B579048>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68B5918C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68B5C6188>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68B5FAF88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68B632B08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68B63FAC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68B677B48>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BAEEA88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BB279C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BB62988>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68BB99988>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C690F92948>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C690FC9908>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C691001888>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C69103A848>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C691074808>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6910AC7C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6910E57C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C69111F788>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C691158708>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C691190708>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6911C76C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C691201688>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C691239688>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C68B88D7C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C691292788>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6912CB788>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C691305748>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C69133E708>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C691375688>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6913AF688>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6913E7648>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C691420608>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C691459608>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6914925C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6914C9508>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C691501508>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C69153C4C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C691575488>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6915AA488>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6915E5408>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C69161E3C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C691656388>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C69168E388>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6916C7348>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6940C02C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6940F92C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694132288>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C69416B248>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6941A3248>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6941DB188>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694214148>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C69424D108>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694284108>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6942BF0C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6942F5048>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694330048>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694363FC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C69439AF88>],
      dtype=object)
In [19]:
dfpAnorm.plot(subplots=True, figsize=(20,40))#,kind='hist',bins=50)
Out[19]:
array([<matplotlib.axes._subplots.AxesSubplot object at 0x000002C690B1C088>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C690C4B988>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6908F1B88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C690906F88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C690922888>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C690958148>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C69098ED88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6909C8248>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6909D4108>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C690A06F08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C690DEEE08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C690E27D48>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C690E5FD08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C690E96CC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C690ECFCC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C690F08C88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C690F3FC08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694A07C08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694A43C08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694A7BBC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694AB3B88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694AEEB88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694B27A88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694B5FA48>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694B97A48>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694BD0A08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694C079C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694C40988>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694C79948>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694CB2908>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694CEB8C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694D248C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694D5D848>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694D95808>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694DCC808>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694E077C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694E40788>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694E77708>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694EB16C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694EEB688>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694F21648>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694F5B648>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694F94608>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C694FCC588>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C695004588>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C69503C548>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C695075508>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6950AE508>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6950E8448>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C695121408>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6979493C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6979813C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6979BB388>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C690BE0108>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C697A0F8C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C697A48888>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C697A80848>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C697ABB848>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C697AF3808>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C697B2C748>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C697B64748>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C697B9C708>],
      dtype=object)
In [20]:
dfpNorm.plot(kind = 'line', figsize=(20,15))
Out[20]:
<matplotlib.axes._subplots.AxesSubplot at 0x2c6984da748>
In [21]:
dfpAnorm.plot(kind = 'line', figsize=(20,15))
Out[21]:
<matplotlib.axes._subplots.AxesSubplot at 0x2c69882f048>
In [22]:
pd.plotting.autocorrelation_plot(dfp.iloc[:,2])
Out[22]:
<matplotlib.axes._subplots.AxesSubplot at 0x2c6988d5808>
In [23]:
dfp.iloc[:,2].autocorr(lag=1)
Out[23]:
0.9699295932047027
In [24]:
# Draw Plot
def plotDfACorr(df):
    f1,ax = plt.subplots(len(df.columns),1 , figsize=(18, 50))
    for i in range(len(df.columns)):
        plot_acf(df.iloc[:,i],ax=ax[i],zero=False,title=df.columns[i])
        ax[i].grid()
    plt.show()
In [25]:
# Draw Plot
def plotDfPCorr(df):
    f1,ax = plt.subplots(len(df.columns),1 , figsize=(18, 50))
    for i in range(len(df.columns)):
        plot_pacf(df.iloc[:,i],ax=ax[i],zero=False,title=df.columns[i])
        ax[i].grid()
    plt.show()
In [26]:
plotDfACorr(dfp)
In [27]:
plotDfACorr(dfpNorm)

C:\ProgramData\Anaconda3\lib\site-packages\statsmodels\tsa\stattools.py:546: RuntimeWarning:

invalid value encountered in true_divide
In [28]:
plotDfACorr(dfpAnorm)

Partial Correlations:

In [29]:
dfp.columns
Out[29]:
Index(['label', 'x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x9', 'x10',
       'x11', 'x12', 'x13', 'x14', 'x15', 'x16', 'x17', 'x18', 'x19', 'x20',
       'x21', 'x22', 'x23', 'x24', 'x25', 'x26', 'x27', 'x28', 'x29', 'x30',
       'x31', 'x32', 'x33', 'x34', 'x35', 'x36', 'x37', 'x38', 'x39', 'x40',
       'x41', 'x42', 'x43', 'x44', 'x45', 'x46', 'x47', 'x48', 'x49', 'x50',
       'x51', 'x52', 'x53', 'x54', 'x55', 'x56', 'x57', 'x58', 'x59', 'x60',
       'x61'],
      dtype='object')
In [30]:
plotDfPCorr(dfp.iloc[:,1:])
In [31]:
plotDfPCorr(dfpNorm.iloc[:,1:])

---------------------------------------------------------------------------
LinAlgError                               Traceback (most recent call last)
<ipython-input-31-e803d942de0b> in <module>
----> 1 plotDfPCorr(dfpNorm.iloc[:,1:])

<ipython-input-25-aaf35f1d4eb6> in plotDfPCorr(df)
      3     f1,ax = plt.subplots(len(df.columns),1 , figsize=(18, 50))
      4     for i in range(len(df.columns)):
----> 5         plot_pacf(df.iloc[:,i],ax=ax[i],zero=False,title=df.columns[i])
      6         ax[i].grid()
      7     plt.show()

C:\ProgramData\Anaconda3\lib\site-packages\statsmodels\graphics\tsaplots.py in plot_pacf(x, ax, lags, alpha, method, use_vlines, title, zero, vlines_kwargs, **kwargs)
    254         acf_x = pacf(x, nlags=nlags, alpha=alpha, method=method)
    255     else:
--> 256         acf_x, confint = pacf(x, nlags=nlags, alpha=alpha, method=method)
    257 
    258     _plot_corr(ax, title, acf_x, confint, lags, irregular, use_vlines,

C:\ProgramData\Anaconda3\lib\site-packages\statsmodels\tsa\stattools.py in pacf(x, nlags, method, alpha)
    799         ret = pacf_ols(x, nlags=nlags, efficient=efficient, unbiased=unbiased)
    800     elif method in ('yw', 'ywu', 'ywunbiased', 'yw_unbiased'):
--> 801         ret = pacf_yw(x, nlags=nlags, method='unbiased')
    802     elif method in ('ywm', 'ywmle', 'yw_mle'):
    803         ret = pacf_yw(x, nlags=nlags, method='mle')

C:\ProgramData\Anaconda3\lib\site-packages\statsmodels\tsa\stattools.py in pacf_yw(x, nlags, method)
    594     pacf = [1.]
    595     for k in range(1, nlags + 1):
--> 596         pacf.append(yule_walker(x, k, method=method)[0][-1])
    597     return np.array(pacf)
    598 

C:\ProgramData\Anaconda3\lib\site-packages\statsmodels\regression\linear_model.py in yule_walker(X, order, method, df, inv, demean)
   1351     R = toeplitz(r[:-1])
   1352 
-> 1353     rho = np.linalg.solve(R, r[1:])
   1354     sigmasq = r[0] - (r[1:]*rho).sum()
   1355     if inv:

C:\ProgramData\Anaconda3\lib\site-packages\numpy\linalg\linalg.py in solve(a, b)
    401     signature = 'DD->D' if isComplexType(t) else 'dd->d'
    402     extobj = get_linalg_error_extobj(_raise_linalgerror_singular)
--> 403     r = gufunc(a, b, signature=signature, extobj=extobj)
    404 
    405     return wrap(r.astype(result_t, copy=False))

C:\ProgramData\Anaconda3\lib\site-packages\numpy\linalg\linalg.py in _raise_linalgerror_singular(err, flag)
     95 
     96 def _raise_linalgerror_singular(err, flag):
---> 97     raise LinAlgError("Singular matrix")
     98 
     99 def _raise_linalgerror_nonposdef(err, flag):

LinAlgError: Singular matrix
In [32]:
plotDfPCorr(dfpAnorm.iloc[:,1:])
In [ ]:
In [33]:
def plotDfLegend(df,kind,bins,dfName="",width=18,height=15):
    f = plt.figure()
    if kind=="hist":
        df.plot(kind=kind, ax=f.gca(),figsize=(width, height),bins=bins,grid=True)
    else:
        df.plot(kind=kind, ax=f.gca(),figsize=(width, height),grid=True)
    plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
    #plt.savefig("photos/distributions/"+str(dfName)+"_"+str(df.name)+".jpg", dpi=300, format='jpg')
    plt.show()
In [34]:
[plotDfLegend(dfp.iloc[:,i],"hist",50,"dfp") for i in range(len(dfp.columns))]
Out[34]:
[None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None]
In [35]:
[plotDfLegend(dfpNorm.iloc[:,i],"hist",50,"dfpNorm") for i in range(len(dfpNorm.columns))]
Out[35]:
[None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None]
In [36]:
[plotDfLegend(dfpAnorm.iloc[:,i],"hist",50,"dfpAnorm") for i in range(len(dfpAnorm.columns))]
Out[36]:
[None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None]
In [ ]:
In [ ]:
# l1=[0,1,2,3]*2
# x1=[0,1,2,3,4,5,6]
# li=np.repeat(x1,3)

# # Draw Plot
# def plotDfSnsDist(dfn,dfa,bins=20):
#     f1,axes = plt.subplots(len(dfn.columns)-1,2 , figsize=(20, 20))#start row=0&& col=0
#     l1=[i for i in range(0,len(dfn.columns))]
#     for (n,a, b, cn,ca) in itertools.zip_longest\  
# (np.arange(1,len(dfasAnormUniq.columns)),li,[0,1,2]*7,['r','g','b']*7):
#         if a== None :
#             break
#         sns.distplot(df.iloc[:,a],ax=axes[b,bb],color=c,label=df.columns[a],bins=bins)
#         sns.despine(left=True)
#         plt.setp(axes, xticks=[])
#         plt.tight_layout()
#     plt.show()
In [37]:
# Draw Plot
def plotDfCompSnsDist(dfn,dfa,bins=20):
    f1,axes = plt.subplots(len(dfn.columns)-1,2 , figsize=(20, 20))#start row=0&& col=0
    l1=[i for i in range(0,len(dfn.columns))]
    for (n,a, b, cn,ca) in itertools.zip_longest\
        (np.arange(1,len(dfn.columns)),np.arange(1,len(dfa.columns)),\
         l1,['g']*int(len(dfn.columns)+1),['r']*int(len(dfn.columns)+1)):
        if n== None :
            break
        sns.distplot(dfn.iloc[:,n],ax=axes[b,0],color=cn,label=dfn.columns[n],bins=bins)
        sns.distplot(dfa.iloc[:,a],ax=axes[b,1],color=ca,label=dfa.columns[a],bins=bins)
        sns.despine(left=True)
        plt.setp(axes, xticks=[])
        plt.tight_layout()
    plt.show()
In [38]:
plotDfCompSnsDist(dfpNorm,dfpAnorm)

C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:14: UserWarning:

Tight layout not applied. tight_layout cannot make axes height small enough to accommodate all axes decorations

C:\ProgramData\Anaconda3\lib\site-packages\statsmodels\nonparametric\kde.py:487: RuntimeWarning:

invalid value encountered in true_divide

C:\ProgramData\Anaconda3\lib\site-packages\statsmodels\nonparametric\kdetools.py:34: RuntimeWarning:

invalid value encountered in double_scalars
In [ ]:
# l1=[0,1,2,3]*2
# x1=[0,1,2,3,4,5,6]
# li=np.repeat(x1,3)

# # Draw Plot
# def plotDfSnsScatter(df):
#     f1,axes = plt.subplots(7,3 , figsize=(20, 20))#start row=0&& col=0
#     for (a, b,bb, c) in itertools.zip_longest\
# (np.arange(1,len(df.columns)),li,[0,1,2]*7,['r','g','b']*7):
#         if a== None :
#             break
#         sns.scatterplot(df.index,df.iloc[:,a],ax=axes[b,bb],color=c,label=df.columns[a])
#         sns.despine(left=True)
#         plt.setp(axes, xticks=[])
#         plt.tight_layout()
#     plt.show()
In [ ]:
In [39]:
def plotDfCompSnsScatter(dfn,dfa):
    f1,axes = plt.subplots(len(dfn.columns)-1,2 , figsize=(20, 100))#start row=0&& col=0
    l1=[i for i in range(0,len(dfn.columns))]
    for (n,a, b, cn,ca) in itertools.zip_longest\
        (np.arange(1,len(dfn.columns)),np.arange(1,len(dfa.columns)),\
         l1,['g']*int(len(dfn.columns)+1),['r']*int(len(dfn.columns)+1)):
        if n== None :
            break
        sns.scatterplot(dfn.index,dfn.iloc[:,n],ax=axes[b,0],color=cn,label=dfn.columns[n])
        sns.scatterplot(dfa.index,dfa.iloc[:,a],ax=axes[b,1],color=ca,label=dfa.columns[a])
        sns.despine(left=True)
        plt.setp(axes, xticks=[])
        plt.tight_layout()
    plt.show()
In [40]:
plotDfCompSnsScatter(dfpNorm,dfpAnorm)
In [ ]:
In [ ]:
In [ ]:
# # fig, ax = plt.subplots(figsize=(10,5))
# # ax.matshow(dfn.corr())
# plt.figure(figsize=(40,7))
# plt.matshow(dfas.corr(), fignum=1)
# #plt.matshow(dfn.corr())
# plt.xticks(range(len(dfas.columns)), dfas.columns,rotation=90)
# plt.yticks(range(len(dfas.columns)), dfas.columns)
# plt.colorbar()
# plt.show()
In [ ]:
In [ ]:
# sns.pairplot(dfasAnorm)
# plt.show()
In [41]:
corr = dfp.corr()
corr.plot(subplots=True, figsize=(20,200),kind='bar',sharex =False,grid=True)
Out[41]:
array([<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C41D80C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C87D4C48>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C881DA48>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C8833F48>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C8842448>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C885D908>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB072248>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB0A8808>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB0B2808>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB0E99C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB152B08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB188B88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB1C1C88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB1FCDC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB232EC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB26BFC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB2A90C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB2E3208>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB31B308>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB355408>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB38D548>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB3C5648>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB3FE6C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB436808>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB46E908>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB4A6A08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB4DEB48>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB518BC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB551CC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB58AE08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB5C3F08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB601048>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB639188>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB673288>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB6AC388>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB6E54C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB71C5C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB757688>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB78E788>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB7C68C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB7FE9C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB838AC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB86FB88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB8A7C88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB8E1D88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB919EC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB952FC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB9920C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CB9CB208>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBA03308>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBA3B408>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBA73548>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBAAB648>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBAE36C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBB1C808>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBB57908>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBB8EA08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBBC6B08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBBFFC08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBC3AD08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBC71E08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6CBCA9F48>],
      dtype=object)
In [42]:
corr = dfpNorm.corr()
corr.plot(subplots=True, figsize=(20,200),kind='bar',sharex =False,grid=True)
Out[42]:
array([<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A64BA108>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6AD438B08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A9CA6B88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A9DC1C88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6AB6170C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8D541C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8C9D748>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8CBC408>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8C9E408>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8CC95C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8CD2748>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A65657C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A65628C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A6559908>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8926B08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8950388>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8929CC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A9B9ADC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A9B94EC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8748048>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A873F148>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C6217208>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C61F5348>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6BEF90448>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C0160808>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C014F688>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6AD835788>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A9D07808>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8FD38C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8EC6A48>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8AD7B48>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8B0EC48>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A96DA6C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A956FE48>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A9D2BF48>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8BB70C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8504908>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8387288>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A70963C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6BC3074C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6BBF225C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C0531708>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6BA1AA788>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A84CDC88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A84C79C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A85CBF08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A7334BC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C0B15C88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C20EBD88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6C056AE88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B84CCF88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A8419C88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B89A6208>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6BA0B42C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6BC3EE308>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B877C488>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B8773608>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6BA702748>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A727F7C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A93468C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A466CA08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6A49DCB08>],
      dtype=object)
In [43]:
corr = dfpAnorm.corr()
corr.plot(subplots=True, figsize=(20,200),kind='bar',sharex =False,grid=True)
Out[43]:
array([<matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6713A08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6AA3E3D88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B69F4A88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B68EA8C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B683B9C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6A89B08>,
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       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6AD4EC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6AFEFC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6B170C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6B2F1C8>,
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       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6EAD888>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6EE8988>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6F20A88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6F59B48>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6F91C48>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B6FC9D48>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B7002E88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B8009F88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B8046088>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B80801C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B80B82C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B80F23C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B812B508>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B8166608>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B819C688>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B81D47C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B820D8C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B82469C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B827EB08>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B82B8B88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B8330C88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B836AD88>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B83A1EC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B8459FC8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B84960C8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B8511208>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B8548308>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x000002C6B8582408>],
      dtype=object)
In [ ]:
# def plotDfCompSnsScatter(dfn,dfa):
#     f1,axes = plt.subplots(len(dfn.columns)-1,2 , figsize=(20, 100))#start row=0&& col=0
#     l1=[i for i in range(0,len(dfn.columns))]
#     for (n,a, b, cn,ca) in itertools.zip_longest\
#         (np.arange(1,len(dfn.columns)),np.arange(1,len(dfa.columns)),\
#          l1,['g']*int(len(dfn.columns)+1),['r']*int(len(dfn.columns)+1)):
#         if n== None :
#             break
#         sns.scatterplot(dfn.index,dfn.iloc[:,n],ax=axes[b,0],color=cn,label=dfn.columns[n])
#         sns.scatterplot(dfa.index,dfa.iloc[:,a],ax=axes[b,1],color=ca,label=dfa.columns[a])
#         sns.despine(left=True)
#         plt.setp(axes, xticks=[])
#         plt.tight_layout()
#     plt.show()
In [44]:
dfpNorm.columns
Out[44]:
Index(['label', 'x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x9', 'x10',
       'x11', 'x12', 'x13', 'x14', 'x15', 'x16', 'x17', 'x18', 'x19', 'x20',
       'x21', 'x22', 'x23', 'x24', 'x25', 'x26', 'x27', 'x28', 'x29', 'x30',
       'x31', 'x32', 'x33', 'x34', 'x35', 'x36', 'x37', 'x38', 'x39', 'x40',
       'x41', 'x42', 'x43', 'x44', 'x45', 'x46', 'x47', 'x48', 'x49', 'x50',
       'x51', 'x52', 'x53', 'x54', 'x55', 'x56', 'x57', 'x58', 'x59', 'x60',
       'x61'],
      dtype='object')
In [45]:
def plotDfCompSnsBar(dfn,dfa):
    fig,axes = plt.subplots(len(dfn.columns),2 , figsize=(20, 200))#start row=0&& col=0
    l1=[i for i in range(0,len(dfn.columns))]
    for (n,a, b,colNormName,colAnormName) in itertools.zip_longest\
        (np.arange(0,len(dfn.columns)),np.arange(0,len(dfa.columns)),\
         l1,dfn.columns,dfa.columns):
        if n== None :
            break
        dfn.iloc[:,n].plot(kind='bar',ax=axes[b,0],color='g',grid=True)
        dfa.iloc[:,a].plot(kind='bar',ax=axes[b,1],color='r',grid=True)
        axes[b,0].legend([colNormName])
        axes[b,1].legend([colAnormName])
        
        plt.tight_layout()
    fig.tight_layout(h_pad=10)
    plt.show()
In [46]:
plotDfCompSnsBar(dfpNorm.iloc[:,1:].corr(),dfpAnorm.iloc[:,1:].corr())
In [ ]:
# # Draw Plot
# l1=[i for i in range(0,19)]
# def plotDfCompSnsLine(dfn,dfa):
#     fig,axes = plt.subplots(18,2 , figsize=(20, 100))#start row=0&& col=0
   
#     for (n,a, b, cn,ca,colNormName,colAnormName) in itertools.zip_longest\
#         (np.arange(1,len(dfn.columns)),np.arange(1,len(dfa.columns)),\
#          l1,['g']*20,['r']*20,dfn.columns,dfa.columns):
#         if n== None :
#             break
# #         dfn.iloc[:,n].plot(kind='line',ax=axes[b,0],color='g')
# #         dfa.iloc[:,a].plot(kind='line',ax=axes[b,1],color='r')
#         axes[b,0].plot(dfn.iloc[:,n] ,color='g')
#         axes[b,1].plot(dfa.iloc[:,a] ,color='r')
#         axes[b,0].legend([colNormName])
#         axes[b,1].legend([colAnormName])
#         axes[b,0].grid(True)
#         axes[b,1].grid(True)
#         plt.tight_layout()
        
#     fig.tight_layout(h_pad=10)
#     plt.show()
In [ ]:
# plotDfCompSnsLine(dfasNormUniq[:500],dfasAnormUniq[:500])
In [ ]:
In [ ]:
In [ ]:
In [47]:
def color_negative_red(val):
    """
    Takes a scalar and returns a string with
    the css property `'color: red'` for negative
    strings, black otherwise.
    """
#     color = 'red' if val < .7 else 'black'
#     color2= 'green' if val >= .7 else 'brown'
    color =''
    if val < .7:
        color = 'red' 
        
    elif val >= .7:
        color ='green'
        
    return 'color: %s' % color

def highlight_max(s):
    '''
    highlight the maximum in a Series yellow.
    '''
    is_min = s == s.min()
    return ['background-color: yellow' if v else '' for v in is_min]

# s = df.style.applymap(color_negative_red)
# df.style.apply(highlight_max)
# df.style.\
#     applymap(color_negative_red).\
#     apply(highlight_max)
In [48]:
corr=dfpNorm.corr()
corr.style.applymap(color_negative_red).apply(highlight_max)
Out[48]:
label x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x24 x25 x26 x27 x28 x29 x30 x31 x32 x33 x34 x35 x36 x37 x38 x39 x40 x41 x42 x43 x44 x45 x46 x47 x48 x49 x50 x51 x52 x53 x54 x55 x56 x57 x58 x59 x60 x61
label nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
x1 nan 1 0.139318 -0.0565441 0.0602891 0.0480106 -0.120154 0.261621 0.340732 -0.0142271 0.0160085 0.511622 0.563777 0.143452 0.119091 -0.0378864 0.48882 0.053944 0.0970786 -0.0307285 0.0340567 0.107106 0.00516743 -0.0317404 -0.352303 0.494269 -0.00810471 0.0673206 -0.208359 -0.272558 -0.0695299 0.00491047 0.509797 -0.186402 -0.191206 0.0216818 0.122649 0.323769 0.0252415 0.0649142 0.231809 0.00926124 0.126037 -0.0373636 -0.0294873 -0.477386 0.0866356 0.19545 0.239859 0.0690823 0.265762 0.490246 0.280374 0.330654 -0.30387 0.0414172 0.0913275 0.326162 0.0145317 0.474509 -0.113276 nan
x2 nan 0.139318 1 0.16831 -0.139047 0.0352364 -0.0322765 -0.0562911 0.389517 -0.0152103 0.0680678 0.364309 0.42751 -0.0779292 0.0178728 0.0357738 0.432256 0.171845 0.155164 -0.180453 -0.0621013 0.127789 0.186826 -0.0189994 -0.0988272 0.344558 0.0674123 0.200011 -0.0803234 0.0373474 -0.172661 0.00191764 0.368031 -0.257282 -0.0378738 -0.0821845 -0.00752687 0.378335 -0.0381105 0.084483 0.306648 -0.00237416 0.0634885 0.0532941 -0.0398664 -0.390502 -0.237085 0.229904 -0.221203 -0.0976915 0.0960661 0.341548 0.111194 0.102683 0.213789 0.303368 0.158201 0.00685851 0.216456 0.345149 -0.129086 nan
x3 nan -0.0565441 0.16831 1 -0.275375 -0.191389 0.00815534 0.284564 0.207489 0.0421202 0.0482957 0.128713 0.28909 -0.163639 -0.018081 0.0691319 0.38395 -0.202347 -0.264494 0.257841 0.0349711 -0.222267 -0.216045 0.0458722 0.077973 0.336359 0.249966 0.138206 0.450224 0.347726 0.24174 0.0121716 0.325596 0.212162 0.0458771 0.156153 0.531967 0.252916 0.0935707 0.0648681 0.345143 -0.013194 -0.0390709 0.0397859 0.186414 -0.323115 0.0651286 -0.0781351 -0.08058 0.117814 -0.0488918 0.305934 -0.0739203 -0.0545718 0.51649 -0.0170637 -0.0168746 -0.0599798 -0.410667 0.334102 0.117571 nan
x4 nan 0.0602891 -0.139047 -0.275375 1 0.241756 0.199256 -0.038605 0.0246039 0.0217083 -0.000190235 -0.0348156 -0.0546766 0.0639484 -0.164219 -0.127363 -0.0617378 0.117143 0.174574 -0.313566 -0.184474 0.108237 0.245012 0.266608 -0.016967 -0.0290543 -0.330305 -0.133219 0.0536604 -0.594793 -0.260467 0.000716112 -0.00306939 -0.326108 0.193999 -0.379567 -0.105045 0.0329285 0.0340205 -0.0064779 -0.136037 0.00755974 -0.0493587 0.0653526 -0.509653 0.0410113 0.212338 0.105275 0.228061 0.0142673 -0.30843 -0.0100763 -0.251466 0.0403539 -0.406099 -0.209787 -0.196512 -0.170823 -0.111771 -0.00676215 -0.295407 nan
x5 nan 0.0480106 0.0352364 -0.191389 0.241756 1 -0.0282365 -0.0147787 0.300233 0.0170035 0.0294111 -0.0088772 0.0253902 0.014151 -0.18358 -0.148825 0.0213286 0.615422 0.659207 -0.569848 -0.230296 0.308643 0.707859 0.641743 -0.349518 -0.0169264 -0.0744811 0.0486765 -0.00391371 -0.0340624 -0.698912 -0.00875224 -0.011132 -0.547206 -0.0462034 -0.261194 0.0687608 0.418729 0.00455699 0.00599544 0.0976368 -0.00333453 0.0215379 0.213784 -0.384428 -0.0257264 -0.0985914 0.162634 0.132074 -0.50181 -0.385475 0.00744209 -0.356368 0.32088 0.0800751 0.069063 -0.363546 -0.401554 0.0513585 -0.00144546 -0.0415871 nan
x6 nan -0.120154 -0.0322765 0.00815534 0.199256 -0.0282365 1 -0.123816 0.0384009 -0.0513028 -0.0601646 -0.0287412 0.0178286 0.612096 0.0518667 0.00499129 -0.090709 0.0607325 0.112021 -0.153649 0.019776 -0.135252 0.0217466 0.0481684 -0.0721502 -0.106145 -0.032242 0.0565584 -0.0274331 0.402161 -0.0455641 -0.00702571 -0.0717225 -0.118955 0.967582 -0.254721 0.0385812 0.0655837 -0.0243436 -0.0403087 0.00684467 -0.00245723 -0.332217 -0.0765862 -0.1535 0.0576385 -0.0541543 0.289869 -0.0551021 -0.0203416 -0.0309407 -0.0744891 0.00349582 0.126189 0.21518 0.241865 0.489522 -0.0143789 -0.205877 -0.0856946 -0.331663 nan
x7 nan 0.261621 -0.0562911 0.284564 -0.038605 -0.0147787 -0.123816 1 0.310503 0.212915 0.103999 0.32745 0.369264 0.0619645 0.00355163 -0.0804031 0.374135 -0.086021 -0.16914 0.241714 0.0670883 -0.0211727 -0.124027 0.128106 -0.182091 0.401602 0.210233 0.140878 0.0369992 -0.0243084 0.14774 0.00029467 0.385027 0.106149 -0.164239 0.199828 0.568135 0.241772 0.207668 0.130811 0.248596 -0.0176266 0.0701649 0.0154443 0.215539 -0.351448 0.192705 0.0358031 0.182338 0.128918 0.0418026 0.379564 0.0296105 0.163389 -0.0589586 -0.00949318 -0.116381 0.103565 -0.323578 0.379356 0.166061 nan
x8 nan 0.340732 0.389517 0.207489 0.0246039 0.300233 0.0384009 0.310503 1 0.0635876 0.186033 0.512955 0.696067 -0.0237612 -0.0233556 -0.0432363 0.648422 0.360101 0.36063 -0.341053 -0.199309 0.160851 0.417736 0.321241 -0.323539 0.552306 0.165006 0.238275 -0.0162713 0.138876 -0.397796 0.00393677 0.576146 -0.373232 -0.00134439 -0.0717855 0.413883 0.890489 0.106657 0.20524 0.460911 -0.00624058 0.118583 0.0448143 -0.234116 -0.608868 -0.131869 0.359777 -0.00444582 -0.303562 -0.0816714 0.569552 -0.060361 0.283283 0.277369 0.371313 -0.196452 -0.113688 -0.133015 0.569729 -0.136417 nan
x9 nan -0.0142271 -0.0152103 0.0421202 0.0217083 0.0170035 -0.0513028 0.212915 0.0635876 1 0.541919 0.0725614 0.0560816 -0.023175 -0.0287949 -0.0310752 0.0520081 0.0161628 -0.00183446 0.00794826 -0.0357967 -0.00391499 0.023017 0.064208 0.01431 0.070977 -0.0407594 -0.0443799 -0.0141844 -0.0512176 -0.0103219 -0.00691342 0.0714848 -0.0224057 -0.0591085 -0.0568086 0.0902424 0.0265536 0.669517 0.392962 0.0301956 0.00474741 0.0452137 -0.0135327 -0.0326809 -0.0656973 0.0362497 0.00411217 0.0390268 -0.0119095 -0.0645854 0.0720553 -0.0531533 -0.019489 -0.0211886 -0.0263898 -0.0369128 -0.0545843 -0.0828691 0.0739987 -0.014105 nan
x10 nan 0.0160085 0.0680678 0.0482957 -0.000190235 0.0294111 -0.0601646 0.103999 0.186033 0.541919 1 0.0671003 0.0990185 -0.0626818 -0.0224986 -0.105281 0.0887337 0.0602285 0.0283288 -0.0164317 -0.0435741 0.00272566 0.0473031 0.0416213 -0.0257356 0.0858146 -0.00239491 0.0110173 -0.00752644 -0.0325068 -0.0573946 -0.0102135 0.08774 -0.0438102 -0.079197 -0.0318858 0.05071 0.107745 0.363351 0.727035 0.0488015 0.0195294 0.109835 -0.0202145 -0.0474575 -0.0961716 -0.0448347 0.0264831 0.00382881 -0.0497488 -0.0207824 0.0862511 -0.0124593 -0.0147923 0.0143014 0.050334 -0.066781 -0.0326002 -0.0241699 0.0895997 -0.0110019 nan
x11 nan 0.511622 0.364309 0.128713 -0.0348156 -0.0088772 -0.0287412 0.32745 0.512955 0.0725614 0.0671003 1 0.825833 0.0883123 0.0946404 0.00466937 0.740862 0.0272475 0.00281223 0.0307682 -0.0364838 -0.0269112 0.00342941 -0.103056 -0.192935 0.78628 0.0658711 0.0701991 -0.21363 -0.089248 -0.0144245 0.0162132 0.797199 -0.0384611 -0.0481513 0.109281 0.131407 0.510895 0.0700671 0.0859702 0.462283 -0.0068911 0.00631804 -0.0160774 0.0727835 -0.791748 -0.0429665 0.127518 0.0511298 0.0257295 0.246045 0.785214 0.26372 0.178923 -0.10011 0.18212 0.124696 0.231218 0.0903505 0.781621 0.0189735 nan
x12 nan 0.563777 0.42751 0.28909 -0.0546766 0.0253902 0.0178286 0.369264 0.696067 0.0560816 0.0990185 0.825833 1 0.0238438 0.061861 -0.000592734 0.899755 0.0800279 0.0416241 0.00935224 -0.0438474 -0.00578689 0.0600424 0.00927593 -0.230376 0.893914 0.152461 0.178353 -0.0664401 0.0251223 -0.0508478 0.0116505 0.906958 -0.0943428 -0.000297165 0.0946469 0.371485 0.698798 0.0614946 0.10758 0.613884 -0.00830278 0.0489038 0.00138094 0.0591602 -0.907747 -0.0426505 0.200499 0.00436064 -0.0110192 0.173687 0.893692 0.187282 0.22831 0.0708808 0.271777 0.0570937 0.156931 -0.0622133 0.889788 -0.0073835 nan
x13 nan 0.143452 -0.0779292 -0.163639 0.0639484 0.014151 0.612096 0.0619645 -0.0237612 -0.023175 -0.0626818 0.0883123 0.0238438 1 0.048796 -0.0494426 -0.0824985 -0.0218088 -0.0531018 -0.0308233 0.106988 -0.0967764 -0.057561 -0.113487 -0.162215 -0.0592611 -0.0360152 -0.0930993 -0.354564 0.15399 0.019185 -0.00772105 -0.0477756 0.000675888 0.572224 -0.0155482 0.0334912 0.00261258 -0.0145285 -0.04121 0.0191856 -0.0010565 -0.613095 -0.114633 0.00912362 0.0433391 -0.0698836 0.198406 0.0281953 -0.00442981 0.205539 -0.0641465 0.281555 0.168528 -0.105863 0.183426 0.536755 0.199851 -0.049764 -0.0643344 -0.175984 nan
x14 nan 0.119091 0.0178728 -0.018081 -0.164219 -0.18358 0.0518667 0.00355163 -0.0233556 -0.0287949 -0.0224986 0.0946404 0.061861 0.048796 1 -0.040846 0.0406475 -0.131423 -0.114466 0.123731 0.0823036 0.000466678 -0.195873 -0.224357 -0.0422442 0.0274276 0.0982407 0.0451391 -0.157163 0.0939108 0.128675 0.0036131 0.0273435 0.0915874 0.0411981 0.15958 -0.0489679 -0.0590131 -0.038951 -0.0157355 -0.0280314 0.00658392 -0.0152595 -0.0279771 0.131478 -0.0324969 -0.0539624 0.12659 0.00658438 0.0514347 0.252496 0.0177358 0.254244 0.0660793 -0.0195572 0.161863 0.190468 0.230492 0.101677 0.0140487 0.0263737 nan
x15 nan -0.0378864 0.0357738 0.0691319 -0.127363 -0.148825 0.00499129 -0.0804031 -0.0432363 -0.0310752 -0.105281 0.00466937 -0.000592734 -0.0494426 -0.040846 1 0.000236495 -0.0816599 -0.0545936 0.0534663 0.0187438 -0.0697958 -0.0776118 -0.102187 0.0875351 -0.027282 0.0787262 0.0527372 0.0410387 0.0436098 0.100809 -0.00453739 -0.0231831 0.000769247 0.0225475 0.0329571 -0.0871013 -0.0742165 -0.00596822 -0.0480716 -0.0752137 -0.0160054 -0.0187236 -0.00588397 0.0610149 0.0169521 -0.0198181 0.0362535 -0.0194361 0.103519 0.0698738 -0.0297801 0.0537214 -0.0570504 0.0817231 0.0677484 0.152843 0.0254657 0.0495683 -0.0294328 -0.0177554 nan
x16 nan 0.48882 0.432256 0.38395 -0.0617378 0.0213286 -0.090709 0.374135 0.648422 0.0520081 0.0887337 0.740862 0.899755 -0.0824985 0.0406475 0.000236495 1 0.0527563 0.00397405 0.0409377 0.0101794 0.0488139 0.0599858 0.0618103 -0.160119 0.88898 0.270459 0.279251 0.0381688 0.00391601 -0.035709 0.0116215 0.893462 -0.0615946 -0.095862 0.198617 0.394552 0.679624 0.0538859 0.0965069 0.675629 -0.0136052 0.0559859 0.0286878 0.0944958 -0.890797 0.000629329 0.155762 -0.0141634 -0.0219042 0.0898071 0.830416 0.0974914 0.165887 0.11282 0.190383 -0.0272142 0.0742435 -0.0647787 0.882892 0.0501863 nan
x17 nan 0.053944 0.171845 -0.202347 0.117143 0.615422 0.0607325 -0.086021 0.360101 0.0161628 0.0602285 0.0272475 0.0800279 -0.0218088 -0.131423 -0.0816599 0.0527563 1 0.790445 -0.644698 -0.181051 0.304658 0.715067 0.576103 -0.434168 -0.0381499 -0.0527963 0.0940321 -0.0282657 0.119106 -0.733208 -0.00222196 -0.0143934 -0.674435 0.0281951 -0.409511 0.00796052 0.398663 0.00409742 0.0644896 0.106044 0.00155615 0.133055 0.213547 -0.433347 -0.0557225 -0.292132 0.326741 0.0547037 -0.508457 -0.270597 -0.0124016 -0.240403 0.442653 0.195777 0.289284 -0.316031 -0.346319 0.0551867 -0.0199799 -0.173293 nan
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x26 nan -0.00810471 0.0674123 0.249966 -0.330305 -0.0744811 -0.032242 0.210233 0.165006 -0.0407594 -0.00239491 0.0658711 0.152461 -0.0360152 0.0982407 0.0787262 0.270459 -0.0527963 -0.107494 0.258153 0.286406 0.0402316 -0.0367846 0.0952452 -0.192129 0.0706216 1 0.717271 0.247243 0.342044 0.146685 0.00625847 0.0471249 0.0858547 -0.0378928 0.457727 0.33299 0.23752 -0.0110601 0.0343144 0.151011 -0.0226014 -0.0398387 -0.0486909 0.393956 -0.0497643 0.0946078 0.301375 -0.0201849 0.0575173 0.151903 0.0341467 0.124595 0.234987 0.409776 0.427044 0.0305721 0.106809 -0.193806 0.0330238 0.191319 nan
x27 nan 0.0673206 0.200011 0.138206 -0.133219 0.0486765 0.0565584 0.140878 0.238275 -0.0443799 0.0110173 0.0701991 0.178353 -0.0930993 0.0451391 0.0527372 0.279251 0.0940321 0.137133 -0.0526798 0.0652799 0.102124 0.128458 0.230951 -0.231785 0.0173213 0.717271 1 0.174461 0.221548 -0.0883318 0.00795425 0.0243143 -0.24523 0.00634677 0.173652 0.30613 0.305014 -0.0176503 0.0280861 0.179665 -0.0188243 0.154568 -0.00791379 0.0128054 -0.02602 0.0891329 0.494327 0.0245032 -0.0254679 -0.0359892 0.0058176 -0.0440156 0.238239 0.46896 0.523203 -0.0080928 -0.0131524 -0.161197 -0.00519205 -0.116754 nan
x28 nan -0.208359 -0.0803234 0.450224 0.0536604 -0.00391371 -0.0274331 0.0369992 -0.0162713 -0.0141844 -0.00752644 -0.21363 -0.0664401 -0.354564 -0.157163 0.0410387 0.0381688 -0.0282657 -0.0470389 0.196156 0.198868 0.0256394 0.000191436 0.374347 0.114395 0.00760569 0.247243 0.174461 1 0.167803 0.0763642 0.00288335 -0.0147921 0.105287 0.0305468 0.044616 0.237035 0.0379534 0.0291802 0.00251047 0.0598296 -0.00543596 0.0349958 0.14145 0.166794 0.0182946 0.142808 -0.143123 -0.151896 0.146007 -0.387956 0.00553467 -0.415525 -0.0486361 0.369549 -0.159229 -0.288521 -0.36971 -0.38428 0.00625547 0.193939 nan
x29 nan -0.272558 0.0373474 0.347726 -0.594793 -0.0340624 0.402161 -0.0243084 0.138876 -0.0512176 -0.0325068 -0.089248 0.0251223 0.15399 0.0939108 0.0436098 0.00391601 0.119106 0.0967883 -0.0438193 0.116216 -0.10578 0.0261815 0.106811 -0.0536032 -0.0754669 0.342044 0.221548 0.167803 1 -0.0261613 -0.00642068 -0.0724457 0.0626475 0.415424 0.0809636 0.268479 0.216968 -0.020399 -0.00927951 0.16233 -0.0101583 -0.157621 -0.0111922 0.176997 0.00693558 -0.247343 0.0842579 -0.256311 -0.271464 -0.0225483 -0.047287 -0.0540626 0.0541521 0.731979 0.313053 0.0445636 -0.150614 -0.179987 -0.0607778 0.034996 nan
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In [ ]:
In [ ]:
corr=dfpAnorm.corr()
corr.style.applymap(color_negative_red).apply(highlight_max)
In [ ]:
# fig, ax = plt.subplots(figsize=(20,7)) 
# mask = np.zeros_like(dfasAnorm.corr())
# mask[np.triu_indices_from(mask)] = 1
# sns.heatmap(dfasAnorm.corr(), mask= mask, ax= ax, annot= True)
In [ ]:
In [ ]:
# import plotly.offline as py
# import plotly.graph_objs as go
# plotly.offline.init_notebook_mode()

# py.iplot([{
#     'x': dfasNormUniq.index[:100],
#     'y': dfasNormUniq[col][:100],
#     'name': col
# }  for col in dfasNormUniq.columns])
In [ ]:
# import plotly.offline as py
# import plotly.graph_objs as go
# plotly.offline.init_notebook_mode()

# py.iplot([{
#     'x': dfasAnorm.index[:100],
#     'y': dfasAnorm[col][:100],
#     'name': col
# }  for col in dfasAnorm.columns])
In [ ]:
# import plotly.graph_objects as go
# #[:100]
# #.index
# #['Timestamp']
# for i,j in zip(dfas0NormCycle1.columns,dfas0AnormCycle1.columns):
#     fig = go.Figure()
#     fig.add_trace(go.Scatter(x=dfas0NormCycle1.index , y=dfas0NormCycle1[i] ,name='dfasNorm',mode='lines'))
#     fig.add_trace(go.Scatter(x=dfas0AnormCycle1.index , y=dfas0AnormCycle1[j] ,name='dfasAnorm',mode='lines'))
#     fig.update_layout(title=f"{i}")
#     fig.show()
#     #fig.write_html(f"{i}_linePlot_uniq.html")
In [ ]:
In [ ]:
# dfEfList=[dfao,dfas]
# # Additive time series:
# # Value = Base Level + Trend + Seasonality + Error

# # Multiplicative Time Series:
# # Value = Base Level x Trend x Seasonality x Error

# from statsmodels.tsa.seasonal import seasonal_decompose
# for f in dfEfList:
    
#     for i in f.columns:
    
#         decomposition = seasonal_decompose(dfao[i][:100], model="additive", freq=30)#model='multiplicative'
    
        
#         fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18,3), constrained_layout=True)
#         fig.subplots_adjust(wspace=0.15)
#         ax1= plt.subplot(121)
#         ax1.plot(decomposition.trend)
#         ax1.set_title("Trend--> "+i+"")
#         ax2 = plt.subplot(122)
#         ax2.plot(decomposition.seasonal)
#         ax2.set_title("Seasonality--> "+i+"")
    

# plt.tight_layout()
# plt.show()
In [ ]:
# from statsmodels.tsa.seasonal import seasonal_decompose
# result = seasonal_decompose(dfao.iloc[:1000,1], model="additive", freq=30)
# result.plot()
# plt.show()
In [ ]:
# # import matplotlib.dates as mdates

# fig, ax = plt.subplots(figsize=(10,7))
# plt.subplots_adjust(hspace=0.5)

# ax0 = plt.subplot(411)
# plt.plot(result.observed)
# ax0.set_title('obs')

# ax1 = plt.subplot(412)
# plt.plot(result.trend)
# ax1.set_title('trend')

# ax2 = plt.subplot(413)
# plt.plot(result.seasonal)
# ax2.set_title('seasonality')

# ax3 = plt.subplot(414)
# plt.plot(result.resid)
# ax3.set_title('residual')
# fig.autofmt_xdate()
In [ ]:
# viridis(n = 8,option = "B")
# colorRampPalette(vir)

# #Putting it together
# corrplot(cor(dfao,method = "color",type = "lower",
#           sig.level = 0.01, insig = "blank",addCoef.col = "green",col = vir(200))
In [ ]:
In [ ]: